10 Tips for Data Visualization

Designing analytics user interfaces for government decision-makers.

The Billion Dollar Gram quantifies data in an easy-to-read visual representation, so users can garner information at first glance.

Dartmouth Atlas lets users toggle between different visualizations. This example shows a map-based visualization of Medicare spending per recipient on the left, and on the right the same data expressed using a scatter plot to quickly show the distribution.

Place Pulse creates “heat maps” showing how people feel about their city.

At first glance, this map from Recovery.gov shows overwhelming clusters of contracts and loans, but as the user zooms in, each layer of data provides context for a specific understanding or “story” for the user.

Ushahidi is an open source platform that gathers data from various sources to aid in disaster relief efforts.

After disaster strikes or government initiatives fail, in hindsight, we see all too often that warning signs were overlooked by decision-makers. Or sophisticated technology was installed, but nobody took the time to learn to use it. It’s often labeled “user error,” or “problem between keyboard and chair.” In analytics, the problem is especially acute — the most sophisticated analytics models in the world are futile unless decision-makers understand and act on the results appropriately.

This problem often arises because designers haven’t truly considered how those using the fancy dashboards, maps or policy visualizations will interact with the analytics. They may become enamored of the model’s power and try to fit every piece of data into it. However, in offering more options and parameters to control the model’s operation, and filling up every pixel of screen real estate, designers can fail to recognize that most government decision-makers are inundated with inputs, pressed for time and can only focus on essentials. As Yale professor and information design guru Edward Tufte wrote, “Clutter and confusion are not attributes of information; they are failures of design.”

In many cases, users can’t answer basic questions like “What should I pay attention to?” and “Now that I’ve seen this, what should I do?” If the answers aren’t readily apparent, the interface and analytics aren’t solving a problem — rather, they might be creating a bigger one. As one federal executive said recently, “No tweet stops bleeding.Unless something has actually changed, it’s just information. What pieces of data are actually going to help us make a better decision?”

Agencies should consider a more user-centric and outcome-centric approach to analytics design to visualize policy problems and guide executives toward better, faster, more informed decisions.

The good news is that government leaders are getting serious about making sense of their data, and constant advances in graphic, mobile and Web technology make it possible to translate “big data” into meaningful, impactful visual interfaces. Using visualization tools to present advanced analytics can help policymakers more easily understand a topic, create an instant connection to unseen layers of data, and provide a sense of scale in dealing with large, complex data sets.

What Do Users Want? Opportunities for Visual Analytics

Effective analytic interfaces facilitate what the military calls “situational awareness:” full awareness of all events relevant to a given situation, how those events correlate, and an ability to project the effects those events might have on the situation. In the same vein, a situationally aware pilot uses information presented in the cockpit to determine the course of action for that situation. The analytics interface is the modern policymaker’s cockpit. Just as cockpits can’t be designed without understanding what pilots need to do to fly an airplane, design of policy visualizations and analytics interfaces should be driven by an understanding of what data users need for decision-making and what recourse to take after reviewing the analytic results. Well designed “analytics cockpits” have four main characteristics that promote situational awareness:

User-based design. Analytics interfaces should support specific types of policy decisions. Because the interfaces are tailored to users’ specific roles, they won’t be asking, “What does that thing do?” or “Why do I care?”

Less is more. Good analytics interfaces show the information most critical to the user — not every piece of information that might be available for analysis.

Sensory cues direct attention. Effective interfaces exploit people’s natural ability to perceive patterns and trends based on position, size, shape, color and movement. These properties highlight important features that might otherwise be lost in a table of numbers.

Interfaces suggest actions. Analytic dashboards alert users to potential performance issues and provide actionable information for policymakers. Effective interfaces provide context to interpret results that suggest what the user might do next and provide mechanisms to facilitate an explanation and further analysis.

Let the Users Lead

Designing user-centered visualizations for policymakers follow much the same approach as other user-centric design: Start by defining user needs (Consider key questions: What’s the policy issue the decision-maker must understand? What key parameters will drive the decision? Who are the stakeholders and will they need to know?), work backward to identify the data analytics that will drive the user’s decisions, then design the interface accordingly. Users feel about interfaces the same way Supreme Court Justice Potter Stewart described obscenity — they can’t define good interfaces but they know them when they see them. If enough users believe an interface is unsatisfactory, the designer is well advised to accept their judgment.

Know Your Audience

Designers should consider the way users prefer to understand information, even in choosing basic analytic approaches. For users to feel comfortable adopting and sharing insights from analytics, they must be able to explain and defend the data. Simpler models are likelier to be adopted than complex models, and linear models more than non-linear models, often even at the cost of sacrificing accuracy. If contemplating giving users the ability to set modeling parameters, designers must ensure users actually want to set those parameters and that they know how to do so (or at least give them default values).

The Dartmouth Atlas Project (DAP), for example, uses simple customization of reports to appeal to users’ preferences. Through an online platform, DAP displays health data to showcase the variation in health-care costs, care options and service utilization across the U.S. health system — leading observers to explore geographic variation in health statistics. DAP helps to connect with a diverse audience by allowing users to analyze a single data set with different visualizations. The default option is a map, but with a few clicks, a user can see the same data expressed as a trend plot, bar chart or scatterplot and with a drop-down bar, the user can switch to a different data set for comparison.

Use Layers to Tell a Story

While style is one form of customization, layering unique data sets on a single visualization can tell a richer narrative and connect users to the data without getting too crowded. On a map, this can be as simple as zooming in and out, but it can also involve drill-downs (choosing a data point and expanding it to show more detail), links and other shortcuts.

This technique is used on Recovery.gov — the White House website for American taxpayers to track how and where federal recovery funds are spent. To drive transparency and accountability, Recovery.gov designers made it easy for users to compare different data sets by making them layers on the visualization platform. For example, one custom view shows grants and loans for job-training programs against unemployment rates. The colors of the map indicate the unemployment rate in a region. The deeper the color, the higher the unemployment rate and, the thicker the cluster, the more projects are in that area.

For users seeking minute details, each point on the map includes additional data for specific projects. This lets different types of users interact with targeted information to understand and analyze the impact of federal investment in their community.

Involve Users in the Design

There are other benefits to involving users in the design of analytic interfaces. Users can become advocates for the technology, growing support within the organization or among the public for the use of the tool. This might lead to more community members participating (and perhaps improving the data), or pointing out potential accessibility considerations. For example, color-blind users may not be able to differentiate between two colors, but they can distinguish between different intensities of a color.

While users often find it difficult to specify in advance what they really want, it’s critical to involve them early and often in designing analytic interfaces. Visualizing policy often means gathering inputs from a variety of stakeholders. Without constant input at all phases in the decision life cycle, it’s easy to overlook critical information. One caution: Avoid relying on a single user for design. Vet the judgments and suggestions of several users to increase the likelihood that the input is representative of the intended user population.

Less Is More

The right visual display can make it much easier to comprehend complex analytics results and enhance user understanding. Good policy visualizations are a filter between the immense amount of data collected and users who want to quickly understand the driving factors and take an informed next step. It is thus imperative to only provide critical information — anything more simply serves as distraction to a busy user.

Keep It Simple

Analytic results shouldn’t be presented to 10 decimal places when the user doesn’t need that level of precision to make a decision or understand a concept. Effective visual interfaces avoid 3-D effects or ornate gauge designs (a.k.a. “chart junk”) when simple numbers, maps or graphs will do.

Data journalist David McCandless’ Billion Dollar Gram offers a visual comparison of the billions of dollars spent by various governments, government agencies, companies and nonprofit organizations. He doesn’t overload the user with information, but instead deliberately filters it to highlight specific issues and groups to create a visual display that can be quickly absorbed. This chart also shows how effective visualizations can help us understand extremely large numbers or abstract information. It’s hard for most people to see the value of several hundred million dollars in the context of billion-dollar programs and trillion-dollar economies, but the Billion Dollar Gram sums it up well.

Be Aware of Multiple Platforms

Beyond filtering for critical information, also consider the platform or device the users will employ to access the display. Will they be at their computer? On a tablet? On the move with a smartphone? Depending on the device, designers should consider a range of factors, including the aspect ratio (think about what happens when you turn your phone sideways), and the amount of information that can be presented given different display sizes. Today’s government employees are more mobile than ever — especially at the executive level where time is short, and ease of understanding is critical. Even British Prime Minister David Cameron is having a custom iPad app developed to help keep track of the most critical information from across the UK government and social media. When designing interactive visualizations, considering these factors can enhance the user experience so that each click and zoom translates into a tap or pinch.

Sensory Cues Direct Attention

When thinking about the type of visualization, designers should consider the relationships inherent in the data to inform the right style elements. Timelines work well for summarizing sequential data; network graphs summarize relationship data; and maps summarize spatial data. Visualizations help allocate the scarcest resource a decision-maker has: his or her attention. These platforms also can engage users who otherwise might not appreciate analytic techniques.

Use Style Tell a Story

The eye can quickly summarize what might otherwise need thousands of numbers to convey, so the mere orientation, shape, size, width, color and spatial position of data can guide users and amplify their understanding of existing relationships. The important thing is to use these relationships to inform, not to distract the user. The Atlas of Economic Complexity uses a variety of visualization techniques to describe how the relationship between exports and the knowledge required to produce them constrains or promotes a country’s development over time. By watching the network evolve over time, policymakers can see how economies evolve, predict where a country is likely to expand next, or consider how certain interventions might accelerate or hinder growth.

The CIA World Factbook, a source for demographics and economic data about foreign nations, offers another good example. IBM designers developed a visualization of select Factbook statistics to effectively highlight the relationships between countries on certain metrics. By using color, scale and style to focus attention, the designers appeal to the user’s sensory queues to lead to simple comparisons between what would otherwise amount to thousands of separate data points.

From Analytics to Action

An analytics interface may be visually appealing, but if it doesn’t stimulate action, it won’t be very effective. Good interfaces provide enough context to let the user know when action might be required or that a decision should be made.

Ushahidi provides an open source, crowd-based mapping service which can overlay maps of affected regions with data gathered from sources like social networking sites, email and text messages. Any piece of relevant information sent by individuals from their mobile phones or Internet connections in a disaster-stricken area can be monitored. Detailed maps can show, for instance, where people are trapped and where to get safe drinking water, thus providing specific information to help emergency personnel and aid workers take action to assist disaster victims.

Ushahidi’s major innovation is to use the beneficiaries of disaster relief — the victims — as contributors to the relief effort platform. While established humanitarian organizations first viewed Ushahidi and its “unofficial” information with skepticism, they now request use of the platform and volunteer mappers in current disaster and conflict areas.

Another interesting approach comes from researchers at the Massachusetts Institute of Technology’s Media Lab Macro Connections Group. The group created Place Pulse, which asks people to vote on two city images — Which is safer? Which is more unique? Which is more upper class? By mapping the impressions, the research initiative creates “heat-maps” of how people feel about their city — information that can effectively inform city planners, police and other public officials working to increase tourism, reduce vandalism or make their city a nicer place to live.

The Payoff for Getting it Right

Analytics can significantly improve government policy and decision-making. They can help to improve the day-to-day lives of the public, support operational efficiency, identify and mitigate risks, and increase program effectiveness in a budget-constrained era. But to benefit from analytics, interfaces that improve situational awareness must be designed. Technological advances have removed the excuses for settling for “ugly data.” By following the simple design principles outlined here, public organizations and their private and nonprofit partners can create interfaces that help people get the information they need, when they need it, to make faster and smarter decisions. ¨

The authors of this article are analytics and government industry professionals at Deloitte Consulting.